A supplier who competes in a market with one or more competitors is faced with the challenge of continuously pricing their goods and services. If a supplier understands the market's responsiveness to price as well as the supplier's cost, a supplier can determine the optimal price that ensures meeting one or more of the following business objectives; a) Maximizing revenue, b) Maximizing Gross Profit, c) Maximizing Earnings Before Income Tax, d) Market share, e) Factory utilization, and more. In addition to determining the optimum price to meet the designated business objective, the supplier may desire a capability to automatically update the optimal price as the market changes, forecast the supplier's financial performance based on the new optimized price, as well as forecast the performance of the supplier's competitors.
Prior art has multiple limitations that not only prevent a supplier from making an initial useable estimate of the optimal price, but also from making an accurate update of the optimal price, and forecasting the financial performance of the supplier and its competitors. The limitations begin with inaccuracies and potentially incorrect assumptions associated with the demand or yield curve, which depicts the relationship between quantity and price. These inaccuracies are the result of one or more of the following problems; a) Limited span in sales order data in which to build the demand curve, b) Lack of statistically relevant sales order data, c) Lack of market relevant sales order data, d) Implicit assumption that the historical and future sales environments remain the same, e) Lack of a rapid method for assessing whether a new optimized price is required as a result of a shift in market demand or pricing, f) Lack of a method of rapidly updating the optimized price calculation.
The demand curve is typically constructed using the supplier's historical sales order data, which limits the extent and completeness of the demand curve. For example, if the supplier behaves as the “low price leader”, the sales order data can only be used to create a demand curve reflecting how the market responds to low pricing.
The demand curve should depict the market's responsiveness to all pricing scenarios, not just those scenarios, previously employed by the company. As a result of using a demand curve constructed using a limited span of sale order data, it is not likely that the optimum price can be determined.
Another challenge in constructing the demand curve is the lack of statistically relevant data. Frequently, there are pieces of sales data which conflict. An example is that one customer was willing to pay $2.23 each for 10,000 units. Another customer, in the identical customer group may demand 11,500 units for $2.23 each, a 15% difference in quantity. This situation is not unusual, especially for opaque markets where one buyer does not see what other buyers are paying and therefore facilitates a supplier charging different unit prices for the same goods or services. The current art attempts to resolve this situation through averaging algorithms and requires sufficient sale order data for statistical relevance. The challenge is that there is seldom-sufficient data to build a statistically relevant demand curve.
Yet another challenge with the current art is that even if the demand curve is statistically relevant, it is not market relevant. Statistical relevance can be assured through a large enough set of sales orders. However, collecting a large set of sales orders may necessitate waiting long periods of time to allow a sufficient number of orders to be accumulated for statistical relevance. During the long collection period, the market may have changed considerably in its responsiveness to pricing. So while the demand curve may have statistical relevance, it is meaningless because it is based on data too old for market relevance. As a consequence, determining an optimum price based on a data demand curve is unlikely.
In the current art, there is an implicit assumption that the historical sales and future sales environments are identical. For example, if the derived demand curve indicates that 10,000 units were sold when the price was $3.25, the expectation going forward is that the supplier will again sell 10,000 units at $3.25. The implicit assumption is that the overall economic environment, the supplier's approach to marketing, and selling methodology has remained the same. Rarely do the economic environment, the supplier's marketing, and selling methodologies remain intact for any length of time. As a consequence, the validity of the demand curve is questionable and its usefulness in doubt.
Without a representative demand curve, it is impossible to determine an optimum price that ensure meeting one or more of the following business objectives; a) Maximizing revenue, b) Maximizing Gross Profit, c) Maximizing Earnings Before Income Tax, d) Market share, e) Factory utilization, etc.
Even if prior art could overcome the aforementioned issues associated with the span of sales order data, statistical relevance, market relevance, and the accommodate changes in selling methodologies, prior art still must overcome the final issue of rapidly determining when market shifts in pricing and demand necessitate updating the demand curve. Without a method for rapidly determining when the demand curve is no longer representative of the market's responsiveness to price, a supplier will continue under the presumption that the current price is optimal when the market shifts have necessitated that a new optimal price is needed.
In accuracies and poor assumptions aside, once a demand curve is created, the supplier can make a determination of how to price their goods and services in order to satisfy certain business objectives. With an understanding of the relationship between quantity and price, an income statement, as well as additional metrics, can be constructed for each price through the following steps; a) Calculation of revenue by multiplying the price and quantity, b) Determination of the cost-of-goods by multiply the quantity and unit cost at that quantity, c) Calculation of gross profit by subtracting the cost-of-goods from the revenue, d) Determining the sales and general administration costs, e) Calculating the earnings before income tax by subtracting the sales and general administration costs from the gross profit, f) Calculation of market share by dividing the quantity by the total quantity sold by all suppliers, and e) Calculating factor utilization by dividing the units sold by the capacity of the factory for that product.
Once the income statement and additional metrics are calculated for each price, the optimum price can be selected to satisfy various business objects. For example, the supplier may wish to optimize pricing to maximize revenue. To identify the optimum price that maximizes revenue, the income statements are searched to identify where the revenue is maximized and the associated price extracted.
In addition to optimizations with one objective in mind, optimizations are possible that maximize the multiple business objectives. For example, the supplier may wish to optimize pricing to maximize revenue and gross profit. In this example, the income statements are searched for the price at which revenue is maximized and the price at which gross profit is maximized. The supplier then selects a price between the maximum gross profit and revenue price that represents the best tradeoff between these two business objectives.
While forecasting an income statement for a supplier using price optimization remains a challenge because of the limitation of prior in creating a demand curve, accurately modeling the financial performance of a supplier with optimization and their competitors is an even steeper challenge. If the optimized supplier lowers their price, sales volume is likely to increase with a corresponding reduction in sales for other suppliers. In the absences of an accurate relationship of price and quantity for any of the suppliers, it is challenging if not impossible to predict the financial performance of the suppliers.